Adaptive algorithm based on a new hyperbolic sine cost function

Ahmad Khalifi; Qadri Mayyala; Naveed Iqbal; Azzedine Zerguine; Karim Abed-Meraim

doi:10.1109/ACSSC.2017.8335459

Adaptive algorithm based on a new hyperbolic sine cost function

Ahmad Khalifi, Qadri Mayyala, Naveed Iqbal, Azzedine Zerguine, Karim Abed-Meraim

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Scopus citations

Abstract

This paper introduces a stochastic gradient algorithm, which uses an exclusive hyperbolic sine objective function. The algorithm belongs to the variable step size (VSS) class. The algorithms in this class are shown to be very stable and effective in many applications, e.g., echo-cancellation, equalization, and others. In this algorithm, as opposed to other existing VSS algorithms, only one tuning parameter is needed. Experimental results show that with a sub-optimal selection of the tuning parameter, the algorithm provides very promising results in both stationary and tracking scenarios. Analytic convergence and steady state error performance analysis are provided to demonstrate the excellent performance. Also, an optimal solution, based on the least hyperbolic sine error, is derived to confirm the convergence of the proposed algorithm towards the Wiener solution.

Original language	English
Title of host publication	Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
Editors	Michael B. Matthews
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	812-815
Number of pages	4
ISBN (Electronic)	9781538618233
DOIs	https://doi.org/10.1109/ACSSC.2017.8335459
State	Published - 2 Jul 2017

Publication series

Name	Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017
Volume	2017-October

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

Keywords

Adaptive filters
FIR
LMS
Non-quadratic cost functions
Time-varying environments
Tracking
Variable step size

ASJC Scopus subject areas

Control and Optimization
Computer Networks and Communications
Hardware and Architecture
Signal Processing
Biomedical Engineering
Instrumentation

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10.1109/ACSSC.2017.8335459

Cite this

Khalifi, A., Mayyala, Q., Iqbal, N., Zerguine, A., & Abed-Meraim, K. (2017). Adaptive algorithm based on a new hyperbolic sine cost function. In M. B. Matthews (Ed.), Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017 (pp. 812-815). (Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017; Vol. 2017-October). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACSSC.2017.8335459

Khalifi, Ahmad ; Mayyala, Qadri ; Iqbal, Naveed et al. / Adaptive algorithm based on a new hyperbolic sine cost function. Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017. editor / Michael B. Matthews. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 812-815 (Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017).

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title = "Adaptive algorithm based on a new hyperbolic sine cost function",

abstract = "This paper introduces a stochastic gradient algorithm, which uses an exclusive hyperbolic sine objective function. The algorithm belongs to the variable step size (VSS) class. The algorithms in this class are shown to be very stable and effective in many applications, e.g., echo-cancellation, equalization, and others. In this algorithm, as opposed to other existing VSS algorithms, only one tuning parameter is needed. Experimental results show that with a sub-optimal selection of the tuning parameter, the algorithm provides very promising results in both stationary and tracking scenarios. Analytic convergence and steady state error performance analysis are provided to demonstrate the excellent performance. Also, an optimal solution, based on the least hyperbolic sine error, is derived to confirm the convergence of the proposed algorithm towards the Wiener solution.",

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author = "Ahmad Khalifi and Qadri Mayyala and Naveed Iqbal and Azzedine Zerguine and Karim Abed-Meraim",

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Khalifi, A, Mayyala, Q, Iqbal, N , Zerguine, A & Abed-Meraim, K 2017, Adaptive algorithm based on a new hyperbolic sine cost function. in MB Matthews (ed.), Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017. Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017, vol. 2017-October, Institute of Electrical and Electronics Engineers Inc., pp. 812-815. https://doi.org/10.1109/ACSSC.2017.8335459

Adaptive algorithm based on a new hyperbolic sine cost function. / Khalifi, Ahmad; Mayyala, Qadri; Iqbal, Naveed et al.
Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017. ed. / Michael B. Matthews. Institute of Electrical and Electronics Engineers Inc., 2017. p. 812-815 (Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017; Vol. 2017-October).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Mayyala, Qadri

AU - Iqbal, Naveed

AU - Zerguine, Azzedine

AU - Abed-Meraim, Karim

PY - 2017/7/2

Y1 - 2017/7/2

N2 - This paper introduces a stochastic gradient algorithm, which uses an exclusive hyperbolic sine objective function. The algorithm belongs to the variable step size (VSS) class. The algorithms in this class are shown to be very stable and effective in many applications, e.g., echo-cancellation, equalization, and others. In this algorithm, as opposed to other existing VSS algorithms, only one tuning parameter is needed. Experimental results show that with a sub-optimal selection of the tuning parameter, the algorithm provides very promising results in both stationary and tracking scenarios. Analytic convergence and steady state error performance analysis are provided to demonstrate the excellent performance. Also, an optimal solution, based on the least hyperbolic sine error, is derived to confirm the convergence of the proposed algorithm towards the Wiener solution.

AB - This paper introduces a stochastic gradient algorithm, which uses an exclusive hyperbolic sine objective function. The algorithm belongs to the variable step size (VSS) class. The algorithms in this class are shown to be very stable and effective in many applications, e.g., echo-cancellation, equalization, and others. In this algorithm, as opposed to other existing VSS algorithms, only one tuning parameter is needed. Experimental results show that with a sub-optimal selection of the tuning parameter, the algorithm provides very promising results in both stationary and tracking scenarios. Analytic convergence and steady state error performance analysis are provided to demonstrate the excellent performance. Also, an optimal solution, based on the least hyperbolic sine error, is derived to confirm the convergence of the proposed algorithm towards the Wiener solution.

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Khalifi A, Mayyala Q, Iqbal N , Zerguine A, Abed-Meraim K. Adaptive algorithm based on a new hyperbolic sine cost function. In Matthews MB, editor, Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. 812-815. (Conference Record of 51st Asilomar Conference on Signals, Systems and Computers, ACSSC 2017). doi: 10.1109/ACSSC.2017.8335459

Adaptive algorithm based on a new hyperbolic sine cost function

Abstract

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Bibliographical note

Keywords

ASJC Scopus subject areas

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